method
active
method:eeg-neural-criticality-measurementEEG Neural Criticality Measurement
Proposed empirical method for testing the criticality prediction in populations reporting stable selflessness
Neighborhood — ranked by edge-count
Methods (1)
method
- fMRI Neural Criticality Measurementrelated_toProposed empirical method alongside EEG for measuring signatures of criticality in post-dual agents
Hypotheses (1)
hypothesis
- Agents who have undergone stable emptiness realisation will exhibit neural dynamics closer to criticality than matched controlsassociated_withPrimary empirical prediction derived from the reduced VFE of the post-dual agent
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Threshold between ordered and chaotic dynamics; predicted to be more prevalent in post-dual agents due to lower VFE
- One of four clinical taxonomy dimensions used to benchmark SAE features
- Michael Johnson's prior work on how neural networks (and brains) can be 'annealed' to find optimal states.
- One of four clinical taxonomy dimensions used to benchmark SAE features
- Minimal set of neural mechanisms jointly sufficient for the occurrence of a conscious experience; dominant neuroscience approach critiqued as insufficient for causal theory
- Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.703Central methodological claim of the paper.
- Benchmarks designed to evaluate AI consciousness, which the paper argues are vulnerable to eval awareness inflation.
- Cognition in nervous systems, used as a modelling target